Combinatorial approaches using an AI/ML-driven drug development platform targeting insulin resistance, lipid dysregulation, and inflammation for the amelioration of metabolic syndrome in patients

Abstract

Dysregulations of key signaling pathways leading to metabolic syndrome (MetS) are complex eventually leading to cardiovascular events and type 2 diabetes. Dyslipidemia induces progression of insulin resistance and provokes release of proinflammatory cytokines resulting in chronic inflammation, acceleration of lipid peroxidation with further development of atherosclerotic alterations and diabetes. We have proposed a novel combinatorial approach using FDA approved compounds targeting IL-17a and DPP4 to ameliorate a significant portion of the clustered clinical risks in patients with MetS. As MetS is considered a multifactorial disorder, the treatment measures cannot be focused on the specific pathway because other metabolic changes keep the pathological processes in progression. In our present research we have modeled an outcomes of metabolic syndrome treatment using two distinct drug classes. Targets were chosen based on the clustered clinical risks in MetS; dyslipidemia, insulin resistance, impaired glucose control, and chronic inflammation. The AI/ML platform, BIOiSIM, was used in narrowing down two different drug classes with distinct mode of action and modalities. Preliminary studies demonstrated that the most promising drugs belong to DPP-4 inhibitors and IL-17A inhibitors. Alogliptin was chosen to be a candidate for regulating glucose control with long term collateral benefit of weight loss and improved lipid profiles. Secukinumab, IL-17A sequestering agent used in treating psoriasis, was selected as a candidate to address inflammatory disorders. Our analysis suggests this novel combinatorial approach has a high likelihood of ameliorating a significant portion of the clustered clinical risk in MetS. Author summaryMetabolic syndrome is a global epidemic affecting a significant population worldwide. This syndrome is the manifestation of clustered clinical conditions that cannot be fully ameliorated with monotherapies. No therapeutic approaches were confirmed to be effective in deceleration of the metabolic syndrome progression. Artificial intelligence driven computation methods were used to predict efficacy of innovative combinatorial therapy using IL-17A sequestering agent and a DPP-4 inhibitor. They are expected to mitigate a significant portion of the clustered risks in metabolic syndrome disrupting key pathological pathways playing important role in development of this syndrome. The main therapeutic effects are related to reduction of the elevated lipid level and high glucose concentration. Combinatorial treatment could potentially stop or reverse a significant portion of the clinical risks in metabolic syndrome globally. Repurposing of approved FDA drugs can have increased likelihood of approval of the new therapeutic regimens and can reach patients faster with reduced costs of treatment.